Subgraph counting the task of determining the number of instances of a query pattern within a large graph lies at the heart of many critical applications, from analyzing financial networks and transportation systems to understanding biological interactions. Despite decades of work yielding efficient algorithmic (AL) solutions and, more recently, machine learning (ML) approaches, a clear comparative understanding is elusive. This gap stems from the absence of a unified evaluation framework, standardized datasets, and accessible ground truths, all of which hinder systematic analysis and fair benchmarking. To overcome these barriers, we introduce BEACON: a comprehensive benchmark designed to rigorously evaluate both AL and ML-based subgraph counting methods. BEACON provides a standardized dataset with verified ground truths, an integrated evaluation environment, and a public leaderboard, enabling reproducible and transparent comparisons across diverse approaches. Our extensive experiments reveal that while AL methods excel in efficiently counting subgraphs on very large graphs, they struggle with complex patterns (e.g., those exceeding six nodes). In contrast, ML methods are capable of handling larger patterns but demand massive graph data inputs and often yield suboptimal accuracy on small, dense graphs. These insights not only highlight the unique strengths and limitations of each approach but also pave the way for future advancements in subgraph counting techniques. Overall, BEACON represents a significant step towards unifying and accelerating research in subgraph counting, encouraging innovative solutions and fostering a deeper understanding of the trade-offs between algorithmic and machine learning paradigms.

本研究解决了子图计数领域缺乏统一评估框架及标准数据集的问题。提出的BEACON基准提供了标准化数据集、验证的基准真相及公开的评估环境，帮助对比算法和机器学习方法。实验表明，算法方法在大图上计数效率高，但对复杂模式表现不佳，而机器学习方法适用于更大图模式但需大量输入数据，且在小型密集图上准确性低。

BEACON：高效准确子图计数的基准